The general issue is to search within images for the presence of targets of various types, whether these be objects or people, the targets exhibiting certain characteristics conforming to a model. For example, this may be a parametric model, such as a ratio between width and height which must be equal to a given value A or a three-dimensional model of the CAD type.
Such a method for detection of targets based on a model becomes difficult to implement in the case of a wide variability in appearance of the targets. For example, the appearance of a person can vary widely depending on their posture or their clothes. The method may even become impossible to implement. For example, the owner of a car park will have great difficulty in detecting trucks as long as he does not possess CAD models of the various types of truck, given that the truck manufacturers do not distribute CAD models of their trucks.
In these cases where the modeling of the targets proves to be difficult or even impossible, a known solution consists in implementing a step of OFF-LINE statistical learning, in other words prior to the use of the detection system, and an ON-LINE classification step, in other words simultaneously with the use of the detection system. In fact, the classification step forms an integral part of the process of detection: if a system for detecting pedestrians is considered, a detection occurs as soon as a target has been classified as “pedestrian”.
The off-line statistical learning step consists in learning to recognize targets thanks to an algorithm which automatically extracts the most relevant parameters of the targets, in order to discriminate it with respect to the other elements that may be present on the images. These discriminating parameters are used later during the simultaneous classification step. The simultaneous classification step is carried out in real time on the most recent images supplied by the cameras. However, the conditions of use during the simultaneous classification step are not necessarily exactly the same as the setup conditions during the off-line learning step. This can be due to factors specific to the detection system. Indeed, the height and/or the inclination of the cameras can vary from one installation to another, for example owing to a variation of height and/or of inclination of the support on which they are fixed. Notably, the angle of inclination of fixed cameras on the front of a vehicle changes according to the loading of the vehicle. But this may also be due to factors external to the detection system. Thus, if a system for detecting pedestrians is considered, the learning step can be carried out with people standing up and cameras mounted truly horizontally on a car. However, on a slope or a passing bump, people have a tendency to lean in order to compensate for the slope, such that their appearance ends up inclined with respect to the learning step. Whether due to factors specific or external to the system, this results in a clear degradation of the performance, notably cases of non-detections, the target observed during the classification step no longer having exactly the same appearance as during the learning step.
One conventional solution consists in carrying out a re-learning step for each configuration of use. However, this solution has many drawbacks: it is notably long and non-automatable and requires a real expertise together with ad hoc tools, which excludes the majority of users. Another conventional solution consists in changing the detection thresholds in order to pick up the undetected targets. One major drawback of this solution is the increase in the number of false alarms. The number of false alarms can be reduced by adding post-processing steps, notably a step for tracking targets. However, the complexity of the software implemented is then much higher and does not guarantee to be able to correct all the errors.
The US patent application published under No US 2008/0310678 A1 discloses a device for detecting pedestrians. This device implements a learning step and a classification step based, amongst other things, on a model of pedestrian appearance. This device has the aforementioned drawbacks, resulting in an expected significant number of false alarms, owing to the variability of the appearance of the pedestrians, uncompensated by a correction system taking into account the differences in configuration between learning and ON-LINE detection.
The article entitled “B-spline modeling of road surfaces with an application to free-space estimation” (A. Wedel et al) discloses a method consisting in representing the surface of a road by a B-spline and in measuring V-disparities in order to detect the obstacles on the road. One major drawback of such a method using a parametric model by B-spline is that it can easily become defective if reality is substantially different from the model. Another drawback of such a method based on the V-disparity is that it does not take into account the variations along the transverse axis and that it is consequently maladapted to generic traffic contexts, in other words other than the road.
The article entitled “Real Time Obstacle Detection in Stereovision on Non Flat Road Geometry Through V-disparity Representation” (R. Labayrade et al) discloses a method consisting in modeling, using stereoscopic images, a road together with the obstacles on the road. One drawback of this method is that it does not allow the obstacles to be classified. Moreover, based on the assumption that the road does not exhibit any oblique inclination with respect to the reference of the camera, this method is unreliable in a generic context of an uncompacted or unsurfaced road.